Multidimensional nuclear magnetic resonance (NMR) spectroscopy is pivotal for pursuing NMR-based structural biology (Cavanagh et al., Protein NMR Spectroscopy Academic Press: San Diego (1996); Wüthrich, NMR of proteins and Nucleic Acids Wiley: New York (1986)). In many instances, it is desirable to obtain multidimensional spectral information as rapidly as possible. First, the costs related to spectrometer usage are reduced and the throughput of samples per NMR spectrometer can be increased. Second, the requirement for longevity of NMR samples is alleviated. Third, a higher time resolution can be achieved to study dynamic processes by multidimensional spectra. The first two objectives are at the heart of NMR-based structural genomics, which aims at establishing NMR spectroscopy as a powerful tool for exploring protein “fold space” and yielding at least one experimental structure for each family of protein sequence homologues (Montelione et al., Nat. Struc. Biol. 7:982-984 (2000)).
Fast acquisition of multidimensional spectra, however, is limited by the need to sample (several) indirect dimensions. This restriction can be coined the “NMR sampling problem”; above a threshold at which the measurement time is long enough to ensure a workable signal-to-noise ratio (S/N), the sampling of indirect dimensions determines the requirement for instrument time. In this “sampling-limited” data collection regime (Szyperski et al., Proc. Natl. Acad. Sci. USA 99:8009-8014 (2002)), valuable instrument time is invested to meet the sampling demand rather than to achieve sufficient “signal averaging.” Hence, techniques to speed up NMR data collection focus on avoiding this regime, that is, they are devised to push data collection into the “sensitivity-limited” regime in order to properly adjust NMR measurement time to sensitivity requirements. In view of the well-known fact that NMR measurement times tend to increase with molecular weight, rapid sampling approaches for accurate adjustment of measurement times on the one hand and methodology developed to study large systems on the other (e.g., transverse relaxation optimized spectroscopy (TROSY; Pervushin et al., Proc. Natl. Acad. Sci. USA 94:12366-12371 (1997)) or protein deuteration (Gardner et al., Ann. Rev. Biophys. Biomol. Struct. 27:357-406 (1998)) are complementary.
The implementation of rapid data collection protocols avoiding sampling limitations requires that the number of acquired free induction decays (FIDs), i.e., the number of data points sampled in the indirect dimensions, is reduced. Notably, phase-sensitive acquisition of an ND Fourier transformation (FT) NMR experiment requires sampling of N−1 indirect dimensions with n1×n2× . . . ×nN−1 complex points, representing 2N−1×(n1×n2× . . . ×nN−1) FIDs. There is a steep increase of the minimal measurement time, Tm, with dimensionality; acquiring 16 complex points in each indirect dimension (with one scan per FID each second) yields Tm(3D)=0.5 hours, Tm(4D)=9.1 hours, Tm(5D)=12 days, and Tm(6D)=1.1 years.
When reducing the number of acquired FIDs, the key challenge is to preserve the multidimensional spectral information that can be obtained by conventional linear sampling with appropriately long maximal evolution times in all indirect dimensions. Moreover, trimming the number of sampled data points may in turn require processing techniques that complement, or replace, widely used Fourier transformation of time domain data.
G-Matrix Fourier Transformation (GFT) NMR Spectroscopy
G-matrix Fourier Transformation (GFT) NMR spectroscopy (Kim et al., J. Am. Chem. Soc. 125:1385-1393 (2003)) represents a generalization of reduced dimensionality (RD) NMR spectroscopy and aims at providing high-dimensional spectral information with both accuracy and speed. GFT NMR spectroscopy results from “modules” derived for RD NMR and combines multiple phase-sensitive RD NMR, multiple “bottom-up” central peak detection, and (time domain) editing of the components of the chemical shift multiplets. This resulting data acquisition scheme requires additional processing of time domain data, the so called “G-matrix” transformation. Hence, the acronym “GFT” indicates a combined G-matrix and Fourier transformation.
The phase-sensitive joint sampling of several indirect dimensions of a high-dimensional NMR experiment requires that the spectral width, SWGFT, in the resulting combined “GFT-dimension” is set to SWGFT=Σκj SWj, where SWj and κj represent, respectively, the jth spectral width and the factor to scale the sampling increments of the jth dimension, which enable adjustment for maximal evolution times (Kim et al., J. Am. Chem. Soc. 125:1385-1393 (2003)). As a result, the “sampling demand” increases only linearly when dimensions are added for joint sampling, that is, the minimal measurement time of a GFT NMR experiment scales with the sum of the number of complex points required to sample the individual dimensions. In sharp contrast, the minimal measurement time of a conventional multidimensional NMR scales with the product of the number of complex points. Hence, employment of GFT NMR makes it possible to reduce measurement times by about an order of magnitude for each dimension that is being added to the joint sampling scheme.
As described in Szyperski et al., J. Biomol. NMR 3:127-132 (1993), Szyperski et al., J. Am. Chem. Soc. 115:9307 (1993), Szyperski et al., J. Magn. Reson. B105:188-191 (1994), and Szyperski et al., J. Magn. Reson. B108:197-203 (1995), RD NMR yields doublets (“peak pairs”) that arise from the joint sampling of two chemical shift evolution periods. In GFT NMR, the joint sampling of several shift evolution periods generates more complicated multiplet structures, which were named “chemical shift multiplets” (Kim et al., J. Am. Chem. Soc. 125:1385-1393 (2003)). If all projected shifts are measured in a cosine-modulated fashion, the components of the chemical shift multiplet are all inphase. Depending on which and how many shifts are measured in a sine-modulated manner, various components become antiphase. Recording of all combinations of cosine and sine modulations then allows the components of the shift multiplet to be edited into subspectra. In particular, G-matrix transformation enables this editing to be performed in the time domain. This is advantageous when linear prediction of time domain data is applied, because the S/N for each multiplet component is increased while a single component remains after editing for each subspectrum.
The GFT NMR formalism embodies a generally applicable NMR data acquisition scheme (Kim et al., J. Am. Chem. Soc. 125:1385-1393 (2003)). If m=K+1 chemical shift evolution periods of an ND experiment are jointly sampled in a single indirect GFT dimension, 2m−1 different (N-K)D spectra represent the GFT NMR experiment containing the information of the parent ND experiment. Hence, such a set of 2m−1 subspectra is named an (N,N-K)D GFT NMR experiment. For example, a (5,2)D HACACONHN GFT NMR experiment can be recorded for a 8.6 kDa protein with four scans per real increment in 138 minutes, i.e., the minimal measurement time with a single scan per increment amounts to 33 minutes. In contrast, a conventional 5D HACACONHN NMR sampled with 10(t1/1Hα)×11(t2/13Cα)×22(t3/13C′)×13(t4/15N)×512(t5/1HN) complex data points would have required 5.8 days of spectrometer time with a single scan per real data point. Thus, a 250-fold reduction in minimal measurement time could be achieved with GFT NMR. Moreover, the processed (5,2)D HACACONHN frequency domain data have a total size of 16 MByte, while a hypothetical 5D spectrum with the same digital resolution would represent a file of 618 GByte. Hence, employment of GFT NMR allows accurate adaptation of measurement times without sacrificing digital resolution.
Chemical shifts are multiply encoded in the shift multiplets registered in GFT NMR experiments. This corresponds to performing statistically independent multiple measurements, so that the chemical shifts can be obtained with high precision (Kim et al., J. Am. Chem. Soc. 125:1385-1393 (2003); Kim et al., J. Biomol. NMR 28:117-130 (2004)). Moreover, the well-defined peak pattern of the shift multiplets allows implementation of robust algorithms for peak picking (Moseley et al., J. Magn. Reson. 170:263-277 (2004)). Both features make GFT NMR highly amenable to automated analysis. Although GFT NMR has been shown to aid in high-throughput protein resonance assignments by enabling both fast and precise acquisition of high dimensional spectral information, spectral overlap can still hamper resonance assignments in large proteins.
NMR of Aromatic Rings in Protein
Aromatic amino acids in proteins have long attracted the attention of structural biologists due to their important role for the hydrophobic core. From structural studies using NMR spectroscopy, it is known that (i) aromatic rings in the molecular core provide a large number of crucial 1H—1H nuclear Overhauser effects (NOEs) required for obtaining a high-quality structure (Wüthrich, NMR of Proteins and Nucleic Acids Wiley: New York (1986); Smith et al., J. Biomol. NMR 8:360-368 (1996); Aghazadeh et al., Nature Struct. Biol. 5:1098-1107 (1998); Clore et al., J. Am. Chem. Soc. 121:6513-6514 (1999); Medek et al., J. Biomol. NMR 18:229-238 (2000); Shen et al., J. Am. Chem. Soc. 127:9085-9099 (2005)) and that (ii) aromatic rings flip about the χ2-angle (Wagner, Quat. Rev. Biophys. 16:1-57 (1983)). The flipping of the rings in the close-packed interior of a protein requires large movements of the surrounding atoms and, thus, provides invaluable information on larger-amplitude motional modes and protein dynamics (Skalicky et al., J. Am. Chem. Soc. 123:388-397 (2001)). Hence, sequence specific NMR assignment of aromatic resonances in proteins is of central importance for NMR-based structural studies.
Prior to the advent of multidimensional NMR spectroscopy, assignments of aromatic rings relied on combined use of one-dimensional (1D) spin decoupling experiments (Wagner et al., J. Magn. Reson. 20:565-569 (1975)), selective chemical modification (Snyder et al., Biochemistry 14:3765-3777 (1975)), or comparison of spectra of homologous proteins (Wagner et al., Eur. J. Biochem. 89:367-377 (1978)). Subsequently, the introduction of 2D [1H, 1H]-NOESY and COSY facilitated resonance assignments in unlabeled proteins. An important addition was 2D [13C, 1H] COSY and 2D [13C, 1H] relayed COSY (Brühwiler et al., J. Magn. Reson. 69:546-551 (1986); Wagner et al., Biochemistry 25:5839-5843 (1986)), which provided higher resolution for the 1H lines of aromatic rings (Wagner et al., J. Mol. Biol. 196 :227-231 (1987)) and which were typically acquired at natural 13C abundance. However, these techniques were limited to small proteins (molecular weight <10 kDa). For proteins containing a large numbers of aromatic residues, spectral overlap in 2D renders complete assignment of the aromatic resonances difficult or impossible.
With the advent of 13C/15N isotope labeling of proteins, numerous additional multidimensional NMR experiments for the assignment of aromatic rings have been proposed (Kay et al., J. Magn. Reson. B101:333-337 (1993); Yamazaki et al., J. Am. Chem. Soc. 115:11054-11055 (1993); Grzesiek et al., J. Am. Chem. Soc. 117:6527-6531 (1995); Zerbe et al., J. Biomol. NMR 7:99-106 (1996); Carlomagno et al., J. Biomol. NMR 8:161-170 (1996); Löhr et al., J. Magn. Reson. B112:259-268 (1996); Whitehead et al., J. Biomol. NMR 9:313-316 (1997); Prompers et al., J. Magn. Reson. 130:68-75 (1998); Löhr et al., J. Biomol. NMR 22:153-164 (2002)). The most commonly used approach is to first obtain spin system assignments within the aromatic rings using one-bond scalar couplings, followed by use of 3D/4D heteronuclear resolved [1H, 1H]-NOESY for linking the aromatic resonances to those of the aliphatic side-chain moieties (Cavanagh et al., Protein NMR Spectroscopy: Principles and Practice Academic Press: San Diego (1996)). Alternatively, scalar couplings can be used to connect aliphatic and aromatic spins via 13Cγ spins (Yamazaki et al., J. Am. Chem. Soc. 115:11054-11055 (1993); Löhr et al., J. Magn. Reson. B112:259-268 (1996); Prompers et al., J. Magn. Reson. 130:68-75 (1998)). In parallel, novel isotope labeling strategies have been developed for aromatic rings which alleviate the loss of sensitivity due to broad 1H lines and passive 13C-13C couplings. These include the reverse labeling scheme (Vuister et al., J. Am. Chem. Soc. 116:9206-9210 (1994)), atom-type specific labeling (Wang et al., J. Am. Chem. Soc. 121:1611-1612 (1999)), biosynthetically directed fractional 13C-labeling (Szyperski et al., J. Biomol. NMR 2:323-334 (1992); Jacob et al., J. Biomol. NMR 24:231-235 (2002)), and selective protonation of aromatic rings in an otherwise fully deuterated protein (Rajesh et al., J. Biomol. NMR 27:81-86 (2003)).
For large proteins, HCCH spectroscopy, first introduced for aliphatic side-chain assignments (Kay et al., J. Am. Chem. Soc. 112:888-889 (1990)), has emerged as an efficient means to accomplish aromatic spin system identification. Experiments in this class include 3D (H)CCH and 3D H(C)CH (Cavanagh et al., Protein NMR Spectroscopy: Principles and Practice Academic Press: San Diego (1996)). Their combination with TROSY (Pervushin et al., Proc. Natl. Acad. Sci. USA 94:12366-12371 (1997)) has been shown to yield higher sensitivity (Pervushin et al., J. Am. Chem. Soc. 120:6394-6400 (1998); Meissner et al., J. Magn. Reson. 139:447-450 (1999)), thereby extending the molecular weight limit of proteins accessible to these experiments (i.e., for proteins in the “sensitivity-limited” regime (Szyperski et al., Proc. Natl. Acad. Sci. USA 99:8009-8014 (2002)). However, HCCH-type experiments suffer from the comparably low dispersion of aromatic 13C/1H shifts, making it advantageous to use 4D HCCH (note that good spectral resolution is also important for exploring aromatic ring flipping since accurate line-widths need to be measured (Skalicky et al., J. Am. Chem. Soc. 123:388-397 (2001)). The 4D experiments would, however, lead to increased minimal measurement times which may lead to sampling-limited data acquisition (Szyperski et al., Proc. Natl. Acad. Sci. USA 99:8009-8014 (2002)). Thus, an implementation that provides 4D information while being suited for both sensitivity and sampling limited data collection has not been available.
Nuclear Overhauser Effect Spectroscopy (NOESY)
Efficient NMR-based protein structure determination (Wüthrich, NMR of Proteins and Nucleic Acids Wiley: New York (1986)) relies on measurement of nuclear Overhauser effects (NOEs), which yield 1H-1H upper distance limit constraints. The assignment of NOEs quite generally depends on having (nearly) complete resonance assignments (Wüthrich, NMR of Proteins and Nucleic Acids Wiley: New York (1986); Cavanagh et al., Protein NMR Spectroscopy Academic Press: San Diego (1996)). However, due to the degeneracy of chemical shifts, the NOE assignment remains a non-trivial task even when complete resonance assignments are available. Nowadays, two approaches are routinely used to solve this “NOE assignment problem”. First, proteins are 15N/13C double labeled (Kainosho, Nature Struc. Biol. 4:858-861 (1997); Acton, Methods Enzymol. 394:210-243 (2005)) so that NOEs can be measured in 3D 15N- or 13C-resolved [1H, 1H]-NOE spectroscopy (NOESY) (Cavanagh et al., Protein NMR Spectroscopy Academic Press: San Diego (1996)). Dispersing NOE signals in a third dimension, which encodes a 13C or a 15N shift, typically allows one to assign for medium-sized proteins ˜15-25% of the NOEs directly based on chemical shift data (compared to only a few percent in 2D [1H, 1H]-NOESY (Wüthrich, NMR of Proteins and Nucleic Acids Wiley: New York (1986); Cavanagh et al., Protein NMR Spectroscopy, Academic Press: San Diego (1996)). Second, an initial structure is calculated which is used in conjunction with the chemical shifts to assign additional NOEs. Several such cycles of structure calculation and NOE assignment are usually performed iteratively until a refined structure is obtained.
Importantly, inaccuracies in the initial fold arising from incorrectly assigned NOEs may result in the mis-assignment of additional NOEs. Hence, proper convergence of the NMR structure determination depends on obtaining an appropriately accurate initial structure, i.e., it is advantageous if the bundle of conformers representing the initial solution structure covers a conformational subspace which overlaps with that of the refined ensemble of conformers. This requirement constitutes a key challenge for reliable automated NOE assignment (Güntert, Prog. NMR Spectroscopy 43:105-125 (2003); Baran et al., Chem. Reviews 104:3451-3455 (2004); Huang et al., Methods Enzymol. 394:111-141 (2005)) and, thus, also for the development of a robust and scalable platform for high-throughput structure determination in structural genomics (Montelione et al., Nature Struc. Biol. 7:982-984 (2000); Yee et al., Proc. Natl. Acad. Sci. USA 99:1825-1830 (2002)). Several programs have been established to automatically obtain accurate initial folds (Güntert, Prog. NMR Spectroscopy 43:105-125 (2003); Baran et al., Chem. Reviews 104:3451-3455 (2004); Huang et al., Methods Enzymol. 394:111-141 (2005)). Among those are AutoStructure (Moseley et al., Methods Enzymol. 339:91-108 (2001); Huang et al., J. Mol. Biol. 327:521-536 (2003); Huang et al., J. Am. Chem. Soc. 127:1665-1674 (2005)) and CYANA (Güntert et al., J. Mol. Biol. 273:283-298 (1997); Herrmann et al., J. Mol. Biol. 319:209-227 (2002); Güntert, Methods Mol. Biol. 278:347-372 (2004)), both of which are widely used. Conceptually, AutoStructure mimics the approach an expert usually takes when solving a structure manually. The initial fold is generated based on (i) intraresidue, sequential, and medium-range NOEs considering NOE patterns of secondary structure, and (ii) unique long-range packing constraints. In contrast, CYANA relies on NOE network-anchoring and combination of (ambiguous) upper distance limit constraints. This led Montelione et al. to classify the AutoStructure and CYANA approaches as being “bottom-up” and “top-down”, respectively (Baran et al., Chem. Reviews 104:3451-3455 (2004)). Since the two programs use distinctly different algorithms, their coupled operation aiming at a consensus NOE assignment promises to further increase the reliability of initial structure calculations.
In the early 1990s, before the more sophisticated computational techniques (Güntert, Prog. NMR Spectroscopy 43:105-125 (2003); Baran et al., Chem. Reviews 104:3451-3455 (2004); Huang et al., Methods Enzymol. 394:111-141 (2005)) mentioned above were established, researchers devised 4D heteronuclear resolved [1H, 1H]-NOESY and explored its impact for NMR structure determination of proteins (Kay et al., Science 249:411-414 (1990); Clore et al., Biochemistry 30:12-18 (1991); Fairbrother et al., Biochemistry 31:4413-4425 (1992); Grzesiek et al., Biochemistry 31:8180-8190 (1992); Archer et al., Biochemistry 32:6680-6687 (1993); Vuister et al., J. Magn. Reson. B101:210-213 (1993)). Such 4D NOESY represents a straightforward and robust approach to tackle the “initial fold problem”; dispersing signals in a fourth dimension enables one to assign the majority of NOEs directly based on chemical shift data. This may yield a highly accurate initial structure so that fast and reliable convergence of the structure determination can be accomplished. However, 4D NOESY suffers from two major drawbacks, which gave a competitive edge to computational methods in recent years. First, an additional heteronuclear polarization transfer needs to be inserted in the radiofrequency (r.f.) pulse scheme. This leads to additional losses arising from transverse relaxation and tends to limit the use of 4D NOESY to small and medium-sized proteins. Second, conventional sampling of three indirect dimensions leads to long (minimal) measurement times. Typically, several days to a week are required to collect a single data set, even when accepting comparably short maximal evolution times (which limits the spectral resolution). This drawback is further exacerbated if a (minimal) radiofrequency (r.f.) phase cycle is employed for artefact suppression (Cavanagh et al., Protein NMR Spectroscopy Academic Press: San Diego (1996)).
The first drawback of 4D NOESY, i.e., its low sensitivity, has been significantly alleviated by the commercial introduction of cryogenic NMR probes (Styles et al., Magn. Reson. 60:397-404 (1994)), which routinely deliver about three-fold higher sensitivity compared to conventional probes (Monleon et al., J. Struc. Func. Genomics 2:93-101 (2002)). Among the various options (Atreya et al., Methods Enzymol. 394:78-108 (2005)) to reduce the long minimal measurement times of heteronuclear NOESY, simultaneous (“time-shared”) acquisition of 15N- and 13C-resolved NOESY (Farmer et al., J. Biomol. NMR 4:673-687 (1994); Pascal et al., J. Magn. Reson. 103:197-201 (1994); Jerala et al., J. Magn. Reson. B108:294-298 (1995); Uhrin et al., J. Biomol. NMR 18:253-259 (2000); Xia et al., J. Biomol. NMR 27:193-203 (2003)), extensive signal aliasing (Morshauser et al., J. Magn. Reson. 139:232-239 (1999)), and the employment of the RD approach (Szyperski et al., J. Am. Chem. Soc. 115:9307-9308 (1993); Szyperski et al., J. Magn. Reson. B105:188-191 (1994); Brutscher et al., J. Magn. Reson. B105:77-82 (1994); Szyperski et al., J. Magn. Reson. B108:197-203 (1995); Szyperski et al., J. Am. Chem. Soc. 118:8146-8147 (1996); Szyperski et al., J. Biomol. NMR 11:387-405 (1998); Szyperski et al., Proc. Natl. Acad. Sci. USA 99:8009-8014 (2002)) have been proposed (Brutscher et al., J. Magn. Reson. B109:397404 (1995); Kupce et al., J. Magn. Reson. 172:330-333 (2004)). (Notably, rapid sampling techniques based on shortening of the relaxation delay between scans, such as longitudinal relaxation optimization (Pervushin et al., J. Am. Chem. Soc. 124:12898-12902 (2002); Atreya et al., Proc. Natl. Acad. Sci. USA 101:9642-9647 (2004)), are not well-suited for NOESY; it is desirable to keep 1H steady state magnetization close to its thermal equilibrium value in order to avoid an extensive modulation of NOE by T1(1H) relaxation.)
Residual Dipolar Couplings
Residual dipolar couplings (RDC) are valuable NMR parameters yielding “orientational” constraints (Prestegard, Nat. Struct. Biol. 5:517-522 (1998)) to study biological macromolecules in solution; RDCs are used for (i) refining and validating NMR solution structures of single domain proteins (Tolman et al., Proc. Natl. Acad. Sci. USA 92:9279-9283 (1995); Tjandra et al., Science 278:1111-1114 (1997); Tolman, Curr. Opin. Struc. Biol. 11:532-539 (2001); Bax, Protein Sci. 12:1-16 (2003); Lipsitz et al., Ann. Rev. Biophys. Biomol. Struct. 33:387-413 (2004); Prestegard et al., Chem. Rev. 104:3519-3540 (2004)), (ii) determining the relative orientation of domains in multi-domain proteins and proteins in macromolecular complexes (Dosset et al., J. Biomol. NMR 20:223-231 (2001); Jain et al., J. Mol. Biol. 343:1379-1389 (2004)), (iii) determining the tertiary fold of a protein when only sparse nuclear Overhauser enhancement (NOE) derived distance constraint networks (Wüthrich, NMR of Proteins and Nucleic Acids Wiley: New York, N.Y. (1986)) can be obtained (Delaglio et al., J. Am. Chem. Soc. 122:2142-2143 (2000); Fowler et al., J. Mol. Biol. 304:447-460 (2000); Mueller et al. J. Mol. Biol. 300:197-212 (2000); Andrec et al., J. Biomol. NMR 21:335-347 (2001); Hus et al., J. Am. Chem. Soc. 123:1541-1542 (2001); Rohl et al., J. Am. Chem. Soc. 124:2723-2729 (2002); Giesen et al., J. Biomol. NMR 25:63-71 (2003)), (iv) supporting the resonance assignment of proteins (Tian et al., J. Am. Chem. Soc. 123:11791-11796 (2001); Zweckstetter et al., J. Am. Chem. Soc. 123:9490-9491 (2001); Jung et al., J. Biomol. NMR 30:25-35 (2004)), and (v) elucidating protein dynamics (Tolman et al., Nat. Struct. Biol. 4:292-297 (1997); Tolman et al., J. Am. Chem. Soc. 123:1416-1424 (2001); Meiler et al., J. Am. Chem. Soc. 125:8072-8073 (2003)). Since RDC-derived structural constraints can be obtained rapidly, they are also attractive for structural genomics (Montelione et al., Nat. Struc. Biol. 7:982-984 (2000)). A dense set of orientational constraints can be obtained if different types of RDCs are considered [for example, 13Cα—1Hα (1DCH), 15N—1HN (1DNH), or 15N—13C′ (1DNC′) couplings]. The tightness of the constraints used for structure calculations depends on (i) the absence of systematic errors that may arise from varying conditions present during NMR data acquisition for the different types of couplings, (ii) the proper identification and assessment of internal motional modes which partially average RDCs (Tolman et al., J. Am. Chem. Soc. 123:1416-1424 (2001); Peti et al., J. Am. Chem. Soc. 124:5822-5833 (2002), and (iii) evidently the precision of the RDC measurement per se (Tolman et al., Proc. Natl. Acad. Sci. USA 92:9279-9283 (1995); Tjandra et al., Science 278:1111-1114 (1997); Tolman et al., J. Am. Chem. Soc. 123:1416-1424 (2001); Bax, Protein Sci. 12:1-16 (2003); Lipsitz et al., Ann. Rev. Biophys. Biomol. Struct. 33:387-413 (2004); Prestegard et al., Chem. Rev. 104:3519-3540 (2004)).
To minimize systematic errors, it is desirable to measure multiple RDCs simultaneously in a single experiment (Wang et al., J. Am. Chem. Soc. 120:7385-7386 (1998); de Alba et al., J. Biomol. NMR 19:63-67 (2001); Bersch et al., J. Biomol. NMR 27:57-67 (2003); Ding et al., J. Am. Chem. Soc. 125:11504-11505 (2003); Permi, J. Biomol. NMR 27:341-349 (2003); Wienk et al., J. Biomol. NMR 25:133-145 (2003); Hoshino et al., J. Magn. Reson. 171:270-276 (2004); Vijayan et al., J. Magn. Reson. 174:245-253 (2005)); this ensures that all couplings are obtained with the same spectrometer set-up and radiofrequency (r.f.) pulse duty cycle. In addition, it would be advantageous to mutually correlate all RDCs and chemical shifts belonging to a given covalent moiety, thereby breaking chemical shift degeneracy. Frequency labeling in a second indirect dimension to disperse signals is then not required, and large sets of unambiguously grouped RDCs can be obtained from two-dimensional (2D) plans exhibiting very high resolution in the indirect dimension. Notably, the shorter minimal measurement times of 2D versus 3D NMR approaches are advantageous when data need to be collected for slowly precipitating aligned protein samples; the different types of couplings, if measured separately, may turn out to be inconsistent with a single alignment tensor (Tolman et al., Proc. Natl. Acad. Sci. USA 92:9279-9283 (1995); Tjandra et al., Science 278:1111-1114 (1997); Tolman, Curr. Opin. Struc. Biol. 11:532-539 (2001); Bax, Protein Sci. 12:1-16 (2003); Lipsitz et al., Ann. Rev. Biophys. Biomol. Struct. 33:387-413 (2004); Prestegard et al., Chem. Rev. 104:3519-3540 (2004)).
Simultaneous measurement of RDCs has been implemented using spin state separation/selection (IPAP (Ottiger et al., J. Magn. Reson. 131:373-378 (1998)), S3E/S3CT (Meissner et al., J. Magn. Reson. 128:92-97 (1997); Sørensen et al., J. Biomol. NMR 10:181-186 (1997)), α/β selection (Andersson et al., J. Biomol. NMR 12:435-441 (1998)) in the indirect dimension in conjunction with E.COSY-type (Andersson et al., J. Biomol. NMR 12:435-441 (1998); Montelione et al., J. Am. Chem. Soc. 111:5474-5475 (1989)) techniques, while TROSY (Pervushin et al., Proc. Natl. Acad. Sci. USA 94:12366-12371 (1997)) can be used to increase the precision of the measurements (Permi, J. Biomol. NMR 27:341-349 (2003); Wienk et al., J. Biomol. NMR 25:133-145 (2003); Hoshino et al., J. Magn. Reson. 171:270-276 (2004)). These experiments suffer, however, from several drawbacks, which are exacerbated if multiple RDCs shall be measured simultaneously: (i) The creation of anti-phase magnetization for spin state separation requires an additional delay (Andersson et al., J. Biomol. NMR 12:435-441 (1998); Ottiger et al., J. Magn. Reson. 131:373-378 (1998)) and results in reduced sensitivity due to transverse relaxation; (ii) In-phase and anti-phase magnetization components relax differentially so that spectral artifacts arise from spin state selection/separation (Ottiger et al., J. Magn. Reson. 131:373-378 (1998)); (iii) When multiple RDCs evolve simultaneously in a non-constant time (Cavanagh et al., Protein NMR Spectroscopy Academic Press: San Diego, Calif. (1996)) fashion, the resulting signals are broadened since transverse relaxation rates add up (Kontaxis et al., J. Magn. Reson. 143:184-196 (2004)). This limits the precision of simultaneous RDC measurements significantly.
The present invention is directed to overcoming the above-noted deficiencies in the art.